Elecccion De Trabajo

Molecular Cell

Forum
How To Choose a Good Scientific Problem
Uri Alon1,*
1Department Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel *Correspondence: urialon@weizmann.ac.il DOI 10.1016/j.molcel.2009.09.013

Choosing good problems is essential for being a good scientist. But what is a good problem, and how do you choose one? The subject is not usually discussedexplicitly within our profession. Scientists are expected to be smart enough to figure it out on their own and through the observation of their teachers. This lack of explicit discussion leaves a vacuum that can lead to approaches such as choosing problems that can give results that merit publication in valued journals, resulting in a job and tenure.
The premise of this essay is that a fullerdiscussion of our topic, including its subjective and emotional aspects, can enrich our science, and our well-being. A good choice means that you can competently discover new knowledge that you find fascinating and that allows selfexpression. We will discuss simple principles of choosing scientific problems that have helped me, my students, and many fellow scientists. These principles might form a basisfor teaching this subject generally to scientists. Starting Point: Choosing a Problem Is an Act of Nurturing What is the goal of starting a lab? It is sometimes easy to pick up a default value, common in current culture, such as ‘‘The goal of my lab is to publish the maximum number of papers of the highest quality.’’ However, in this essay, we will frame the goal differently: ‘‘A lab is anurturing environment that aims to maximize the potential of students as scientists and as human beings.’’ Choices such as these are crucial. From values—even if they are not consciously stated—flow all of the decisions made in the lab, big and small: how the lab looks, when students can take a vacation, and (as we will now discuss) what problems to choose. Within the nurturing lab, we aim to choose aproblem for our students (and for ourselves) in order to foster growth and self-motivated research. The Two Dimensions of Problem Choice To choose a scientific problem, let us begin with a simple graph, as a starting point for discussion (Figure 1). We will compare problems by imagining two axes. The first is feasibility—that is, whether a problem is hard or easy, in units such as the expected time tocomplete the project. This axis is a function of the skills of the researchers and of the technology in the lab. It is important to remember that problems that are easy on paper are often hard in reality, and that problems that are hard on paper are nearly impossible in reality. The second axis is interest: the increase in knowledge expected from the project. We generally value science thatventures deep into unknown waters. Problems can be ranked in terms of the distance from the known shores, by the amount in which they increase verifiable knowledge. We will call this the interest of the problem. In a forthcoming section, we will discuss the subjective nature of the interest axis. But first, let us first consider aspects of problem choice using our diagram. Looking at the range of problemsin this two-dimensional space, one sees that many projects in current research are of the easy-but-not-too-interesting variety, also known as ‘‘low-hanging fruit.’’ Many other projects in science today are unfortunately both difficult and have low interest, partially stemming from a view that hard equals good. A few problems are grand challenges: tough problems with the potential to considerablyadvance understanding. But most often we would like problems in the top-right quadrant, both feasible and with high interest, likely to extend our knowledge significantly. The diagram suggests a way to choose between problems, using the Pareto front principle of optimization theory. If problem A is better on both axes than problem B, one can erase B from the diagram. Applying this criterion to all...